Systems and Methods to Evaluate Accuracy of Locations of Mobile Devices

ABSTRACT

Systems and methods to identify a region in which a mobile device is located, by: applying device attributes of the mobile device and region attributes of coordinates of the mobile device to a predictive model to generate an accuracy indicator, identifying a plurality of locations from the coordinates of the mobile device that represent one point and the accuracy indicator, converting coordinates of the locations to cell identifiers of a grid reference system, determining region(s) containing the locations by finding matching cell identifiers that are pre-associated with the region(s), and determining a confidence level of a region that contains at least a portion of the locations based on the weights of locations in the portion. The region is identified in response to a determination that the confidence level is above a threshold.

RELATED APPLICATIONS

The present application is a continuation application of U.S. patentapplication Ser. No. 15/716,097, filed Sep. 26, 2017 and issued as U.S.Pat. No. 10,117,216 on Oct. 30, 2018, the entire disclosure of whichapplication is hereby incorporated herein by reference.

The present application relates to U.S. patent application Ser. No.15/016,067, filed Feb. 4, 2016 and issued as U.S. Pat. No. 9,756,465 onSep. 5, 2017, which is a continuation application of U.S. patentapplication Ser. No. 14/593,947, filed Jan. 9, 2015 and issued as U.S.Pat. No. 9,307,360 on Apr. 5, 2016, the entire contents of whichapplications are hereby incorporated by reference as if fully set forthherein.

FIELD OF THE TECHNOLOGY

At least one embodiment of the disclosure relates to the determinationof regions in which mobile devices are located in general and morespecifically but not limited to, computational efficient ways toidentify predefined regions in which mobile devices are located.

BACKGROUND

A location determination system, such as a Global Positioning System(GPS), allows a mobile device, such as a mobile phone, a smart phone, apersonal media player, a GPS receiver, etc., to determine its currentlocation on the earth. The location of the mobile device is typicallycalculated as a set of coordinates, such as the longitude and latitudecoordinates of a point on the surface of the earth.

However, the location of the mobile device in the form of coordinates ofa point on the surface of the earth does not provide sufficientinformation of interest about the location, such as whether the mobiledevice is within a particular region associated with a set of knownproperties.

For example, it may be of interest in certain applications to determinewhether the location of the mobile device is within the store of amerchant, within the home of the user of the mobile device, within arecreation area, within a commercial district, etc.

For example, U.S. Pat. App. Pub. No. 2014/0012806, published Jan. 9,2014 and entitled “Location Graph Based Derivation of Attributes”,discusses the generation of a user profile based on mapping thelocations of a mobile device to predefined geographical regions and usethe attributes associated with the predefined geographical regions toderive and/or augment the attributes of the user profile.

For example, U.S. Pat. App. Pub. No. 2008/0248815, published Oct. 9,2008 and entitled “Systems and Methods to Target Predictive Locationbased Content and Track Conversions”, discusses the need to analyze thelocation of a mobile device to determine the types of businesses thatthe user of the mobile device typically visits, or visited. When thelocation of a mobile device is within a predefined distance from eitherthe address of a particular business or a geographic location associatedwith the business, or within a geometric perimeter of the particularbusiness location, it may be determined that the user of the mobiledevice was at the particular business.

Ray Casting is a known technology to determine whether a given point iswithin a polygon represented by a set of vertexes. However, Ray Castingis computational intensive involving floating point number computations.

The Military Grid Reference System (MGRS) is a standard used forlocating points on the earth. It uses grid squares of various lengths atdifferent resolutions, such as 10 km, 1 km, 100 m, 10 m, or 1 m,depending on the precision of the coordinates provided. A MGRScoordinate includes a numerical location within a 100,000 meter square,specified as n+n digits, where the first n digits give the easting inmeters, and the second n digits give the northing in meters.

The disclosures of the above discussed patent documents are herebyincorporated herein by reference.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments are illustrated by way of example and not limitation inthe figures of the accompanying drawings in which like referencesindicate similar elements.

FIG. 1 shows a system to determine whether a mobile device is within aregion having a predetermined geographical boundary according to oneembodiment.

FIGS. 2-4 illustrate a grid system used to determine whether a locationof a mobile device is within the geographical boundary of a regionaccording to one embodiment.

FIGS. 5-7 illustrate a hierarchical grid system used to determinewhether a location of a mobile device is within the geographicalboundary of a region according to one embodiment.

FIGS. 8 and 9 show a top level grid and the identification of cellswithin the grid according to one embodiment.

FIG. 10 shows an intermediate level grid and the identification of cellswithin the grid according to one embodiment.

FIG. 11 shows the identification of cells within a grid having thefinest resolution in a grid hierarchy according to one embodiment.

FIG. 12 shows the method to determine whether a location of a mobiledevice is within the geographical boundary of a region according to oneembodiment.

FIG. 13 illustrates an example of converting the coordinates of alocation to an identifier of a cell and converting the identifier of thecell to the coordinates of a vertex of the cell according to oneembodiment.

FIG. 14 shows a system configured to map a location of a mobile deviceto one or more identifications of regions according to one embodiment.

FIG. 15 illustrates a data processing system according to oneembodiment.

FIG. 16 shows a method of mapping a location of a mobile device to aregion according to one embodiment.

FIG. 17 illustrates the measurement of the location of the mobile devicewith a level of uncertainty.

FIG. 18 illustrates a method to compute the confidence level of a mobiledevice being in a region having a predefined geographical boundary.

FIG. 19 shows a method to identify regions with corresponding confidencelevels of a mobile device being in the respective regions.

FIG. 20 illustrates region attributes and device attributes for theestimation of the accuracy level of coordinates of a location.

FIG. 21 shows a method to evaluate the accuracy level of coordinates ofa location using a predictive model.

DETAILED DESCRIPTION

The following description and drawings are illustrative and are not tobe construed as limiting. Numerous specific details are described toprovide a thorough understanding. However, in certain instances, wellknown or conventional details are not described in order to avoidobscuring the description. References to one or an embodiment in thepresent disclosure are not necessarily references to the sameembodiment; and, such references mean at least one.

One embodiment of the disclosure provides a computationally efficientmethod and system to determine whether a location of the mobile deviceis within a predetermined geographical boundary of a region and/or todetermine, among a plurality of predefined regions, the identity of oneor more regions within which the location of the mobile device ispositioned.

FIG. 1 shows a system to determine whether a mobile device is within aregion having a predetermined geographical boundary according to oneembodiment.

In FIG. 1, a location determination system uses the wireless signals(e.g., 179) transmitted to and/or from the mobile device (109) todetermine the location (111) of the mobile device (109) on the surfaceof the earth.

For example, the location determination system may use GlobalPositioning System (GPS) satellites (e.g., 117) (and/or base stations(e.g., 115)) to provide GPS signals to the mobile device (109). Themobile device (109) is configured to determine the location (111) of themobile device (109) based on the received GPS signals. In general,multiple GPS satellites (e.g., 117) and/or base stations (e.g., 115) areused to provide the wireless signals (e.g., 179) from differentlocations for a GPS receiver to determine its locations.

In FIG. 1, the mobile device (109) is configured with a cellularcommunications transceiver to communicate with the base stations (e.g.,113, 115) of a cellular communications network.

For example, in one embodiment, the mobile device (109) is configured touse signal delays in the cellular communications signals to or from aplurality of cellular base stations (e.g., 113, . . . , 115) to computethe location coordinates of the mobile device (109).

In FIG. 1, a server (187) is configured to communicate with the mobiledevice (109) via the network (189) and the cellular communicationsinfrastructure (e.g., the base station (113)). The server (187) isconnected to a database (181) storing information about the predefinedregions (e.g., 101, 103, . . . 105, 107).

For example, the database (181) is configured to store theidentifications of a set of cells that are within the boundary of aregion (e.g., 101). The server (187) is configured to convert thelocation (111) of the mobile device (109) to a cell identification andsearch the identifications of the set of cells representing the region(101) to determine if the cell identification converted from thelocation (111) of the mobile device (109) is in the set of cellidentifications representing the region (101). If the cellidentification of the location (111) is found in the set of cellidentifications representing the region (101), the location (111) isconsidered being within the boundary of the region (e.g., 101).

In one embodiment, a hierarchical grid system is used to construct cellsthat are within the boundary of the region (e.g., 101). Thus, the numberof cells within the region (e.g., 101) can be reduced, while theprecision of the determination can be selected at a desired level (e.g.,1 meter).

In one embodiment, the identifications of the cells are configured to besigned integer numbers. Thus, any known technologies for searching agiven number within a set of signed integer numbers can be used toefficiently determine whether the cell identifier of a location (111) iswithin the set of cell identifiers of the region (101).

In one embodiment, the conversion of the location coordinates to a cellidentifier is configured for improved computation efficiency. The cellidentifier is also configured for efficient determination of theresolution of the grid in which the cell is located, the coordinates ofthe vertexes of the cell, the bounding boxes of the cell, and theidentifications of the neighbors of the cells. Details and examples areprovided below.

In one embodiment, a given region (e.g., polygon) on earth isrepresented by a set of cells in a hierarchical, regular grid in alongitude latitude space. In the longitude latitude space, the cells areuniform rectangles/squares at a given resolution; the cell identifiesare constructed from the digits of the longitude/latitude coordinatesfor improved efficiency in conversion between coordinates and cellidentifiers. In one embodiment, the resolution levels of the gridscorrespond to the precision of the longitude/latitude coordinates interms of the number of digits used to after the decimal point torepresent the longitude/latitude coordinates.

At a given resolution in the grid, the identity of the cell thatcontains a given point identified by a longitude/latitude pair can becomputed via simple manipulations of the digits of thelongitude/latitude pair, as illustrated in FIG. 13.

FIGS. 2-4 illustrate a grid system used to determine whether a locationof a mobile device is within the geographical boundary of a regionaccording to one embodiment.

In FIG. 2, a grid (121) of cells is used to identify an approximation ofthe region (101) at a given level of resolution of the grid (121). Theresolution level corresponds to the size of the cells in the grid (121).

In FIG. 2, the region (101) is represented as a polygon having a set ofvertexes (e.g., 123). The set of line segments connecting theneighboring vertexes (e.g., 123) of the region (101) defines theboundary of the region (101).

FIG. 3 illustrates the selection of a set of cells (e.g., 127) that areconsidered to be within the boundary of the region (101). Variousdifferent methods and/or criteria can be used to classify whether a cellis within the boundary of the region (101), especially the cells thatare partially in the region (101) and contain a portion of the boundaryof the region (101). The disclosure of the present application is notlimited to a particular way to identify or classify whether a cell thatis within the boundary of the region (101).

For example, a cell may be classified as being with the region (101)when the overlapping common portion between the cell and the region(101) is above a predetermined percentage of the area of the cell.

For example, a cell may be classified as being with the region (101)when a length of one or more segments of the region (101) going throughthe cell is above a threshold.

For example, the vertexes of the region (101) may be mapped to thenearest grid points to determine an approximation of the boundary of theregion (101) that aligns with the grid lines to select the cells thatare located within the approximated boundary of the region (101).

FIG. 4 illustrates the determination of the location (111) within theset of cells (131, . . . , 133, . . . , 139) according to oneembodiment. In FIG. 4, each of the cells (131, . . . , 133, . . . , 139)represents a portion of the region (101). To determine whether thelocation (111) is within the boundary of the region (101), the system isconfigured to determine whether the set of cells (131, . . . , 133, . .. , 139) contains the location (111).

In one embodiment, to efficiently determine whether any of the cells(131, . . . , 133, . . . , 139) contains the location (111), each of thecells (131, . . . , 133, . . . , 139) is assigned a cell identifier. Inone embodiment, each of the cell identifier is a signed integer forimproved computation efficiency; and the cell identifier is configuredin such a way that the coordinates of any location within the cell canbe manipulated via a set of predetermined, computationally efficientrules to provide the same cell identifier, as further illustrated inFIGS. 12 and 13.

In FIG. 4, after the coordinates of the location (111) is converted tothe cell identifier of the cell (133) that contains the location (111),the system determines whether the location (111) is within the regioncorresponding to the set of cells (131, . . . , 133, . . . , 139) bysearching in the cell identifiers of the set of cells (131, . . . , 133,. . . , 139) representative of the region (101) to find a match to thecell identifier of the cell (133) that is converted from the coordinatesof the location (111). If a match is found, the location (111) isdetermined to be within the region (101); otherwise, the location (111)is determined to be outside of the region (101).

To improve the accuracy in the approximation of the region (101) andcomputational efficiency, the cells of a hierarchical grid system isused to approximate the region (101). FIGS. 5-7 illustrate ahierarchical grid system used to determine whether a location of amobile device is within the geographical boundary of a region accordingto one embodiment.

In FIG. 5, grids of different resolutions are used to identify a set ofcells to approximate the region (101). The grids has a predeterminedhierarchy, in which the grid lines of a high level grid aligns with someof the grid lines of a low level grid such that the cells of the lowlevel grid subdivide the cells of the high level grid. The grids ofdifferent resolutions have different cell sizes.

In general, a grid having a higher resolution and thus smaller cell sizecan approximate the region (101) in better precision, but uses morecells.

In one embodiment, the cells from the lower resolution grid is used inthe interior of the region (101) to reduce the number of cells used; andthe cells from the higher resolution grid is used near the boundary ofthe region (101) to improve precision in using the set of cells toapproximately represent the region (101).

For example, in one embodiment, the lowest resolution gird is applied toidentify a set of cells to approximate the region (101). The cells inthe lowest resolution grid that contain the boundary of the region (101)are split in accordance with the grid of the next resolution level toidentify cells in the grid of the next resolution level for improvedprecision in representing the region (101). The cell splitting processcan be repeated for further improved precision using a higher resolutiongrid.

FIG. 6 illustrates the use of cells from two levels of hierarchicalgrids to approximate the region (101).

After the set of cells used to approximate the region (101) areidentified (e.g., as illustrated FIG. 6), the system is configured todetermine whether the location (111) of the mobile device (109) iswithin the region (101) based on whether any of the set of cellsrepresenting the region contains the location (111), in a way asillustrated in FIG. 7.

For example, in one embodiment, each of the cells used in FIG. 7 torepresent a part of the region (101) is provided with a cell identifier.The coordinates of the location (111) is mapped to a cell identifier ata given resolution level. The system is configured to search in the setof cell identifiers of region (101) at the corresponding resolutionlevel to determine whether there is a match to the cell identifier asdetermined from the coordinates of the location (111). If a match incell identifier is found at any resolution level, the location (111) isdetermined to be within the region (101) represented by the set ofcells; otherwise, the location (111) is determined to be outside theboundary of the region (101).

In one embodiment of FIG. 1, a hierarchical grid system is used toapproximate the predefined regions (101, 103, . . . , 105, 106) withcells. Each of the cells is classified/identified as being in one ormore of the regions (101, 103, . . . , 105, 106). The database (181)stores the identifiers of the cells in association with the identifiesof the respective regions (101, 103, . . . , 105, 106); and the server(187) is configured to compute the identifiers of the cells of differentresolutions that contain the location (111) and determine if any of thecell identifiers stored in the database (181) in association with theidentifiers of the regions (101, 103, . . . , 105, 106) has the samecell identifier as the location (111). If a matching cell identifier isfound, the location (111) of the mobile device (109) is determined to bewith the respective region(s) (e.g., 101) associated with thecorresponding cell identifier; otherwise, the location (111) isdetermined to be outside all of the regions (101, 103, . . . , 105, 106)represented by the set of cell identifiers stored in the database (181).

FIGS. 8 and 9 show a top level grid and the identification of cellswithin the grid according to one embodiment.

In one embodiment, the location (111) of the mobile device (109) isdetermined to be on the surface of the earth in terms of the longitudeand latitude coordinates. In a coordinate system as illustrated in FIG.8, the longitude coordinates are configured to be within the range of−180 degrees to 180 degrees; and the latitude coordinates are configuredto be with the range of −90 degrees to 90 degrees.

In one embodiment, a hierarchical grid system on the surface of theearth is based on a regular grid in the longitude latitude spaceillustrated in FIG. 9.

In FIG. 9, the cells in the top level grid have a uniform size of 10degrees in longitude and 10 degrees in latitude. In FIG. 9, the cellsare identified by the row identifiers ranging from −9 to −1 and 1 to 9and column identifiers ranging from 1 to 36.

In FIG. 9, the row and column identifiers are configured in a way toavoid using zero as a row identifier or a column identifier.

In FIG. 9, the row identifier of 1 is assigned to the row of cellsbetween 0 to 10 degrees of latitude; the row identifier of 2 is assignedto the row of cells between 10 to 20 degrees of latitude; etc. The rowsof cells between 0 to −90 degrees of latitudes are assigned similar rowidentifiers with a negative sign. For example, the row identifier of −1is assigned to the row of cells between 0 to −10 degrees of latitude;the row identifier of −2 is assigned to the row of cells between −10 to−20 degrees of latitude; etc. As a result, the row identifier has a signand a single digit for the top level cells illustrated in FIG. 9; andthe single digit is not zero for any of the rows. Thus, for eachlocation that is inside a cell in the top level grid as illustrated inFIG. 9, the row identifier of the cell containing the location has thesame sign as the latitude coordinate of the location and the singledigit that equals to 1 plus the tens digit of the latitude coordinate ofthe location.

In FIG. 9, the column identifier of 1 is assigned to the column of cellshaving longitude coordinates between −180 to −170 degrees; the columnidentifier of 2 is assigned to the column of cells having longitudecoordinates between −170 to −160 degrees; etc. Thus, for each locationthat is inside a cell in the top level grid as illustrated in FIG. 9,the column identifier of the cell containing the location has no sign(e.g., the column identifier is always greater than zero) and one or twodigits that correspond to adding 18 to a number formed by using thehundreds digit of the longitude as the tens digit and the tens digit ofthe longitude as the ones digit.

The combination of the row identifier and the column identifier of acell uniquely identifies the cell within the top level grid asillustrated in FIG. 9. For example, the digits of the column identifiercan be appended to the row identifier to generate a signed number thatuniquely identifies the cell within the grid illustrated in FIG. 9. Fora given cell identifier, the row identifier and the column identifiercan be unambiguously deduced from the cell identifier itself, since therow identifier has a signal digit and a sign. The longitude and latitudecoordinates of the vertexes of the cell can be computed from the rowidentifier and the column identifier.

Although FIG. 9 illustrates a preferred way to code the row identifiersand the column identifiers based on the longitude and latitudecoordinates of the locations within the cells, alternative codingschemes can be used.

For example, the rows can be coded from 1 to 18 for latitudes from −90degrees to 90 degrees; and the columns can be coded from 10 to 45 forlongitudes from −180 degrees to 180 degrees. Thus, both the row andcolumn identifiers are positive integers, while the column identifiersalways have two digits.

For example, the rows can be coded from 11 to 28 for latitudes from −90degrees to 90 degrees; and the columns can be coded from 11 to 46 forlongitudes from −180 degrees to 180 degrees. Thus, both the row andcolumn identifiers are positive integers having two digits.

FIG. 10 shows an intermediate level grid and the identification of cellswithin the grid according to one embodiment. In FIG. 10, a given cell ata higher level grid (e.g., a cell in the top level grid as illustratedin FIG. 9) is subdivided into 10 rows and 10 columns. The coding of therows and columns correspond to the measurement directions of thelongitude and latitudes coordinates such that the corresponding digitsin the longitude and latitudes coordinates at a given precision levelcan be used directly as the row and column identifiers of the sub-cellswithin the cell at the higher level grid.

For example, when the cell that is being subdivided into the 10 rows and10 columns has a size of 10 degrees in longitude and 10 degrees inlatitude (e.g., as illustrated in FIG. 9), the row identifier and columnidentifier of the sub-cells correspond to the ones digit of the latitudeand longitude coordinates of the points within the respective sub-cells.

For example, when the cell that is being subdivided into the 10 rows and10 columns has a size of 1 degree in longitude and 1 degree in latitude,the row identifier and column identifier of the sub-cells correspond tothe one-tens digit of the latitude and longitude coordinates of thepoints within the respective sub-cells.

FIG. 11 shows the identification of cells within a grid having thefinest resolution in a grid hierarchy according to one embodiment. InFIG. 11, the row identifiers and column identifiers are padded by 1, incomparison with the row and column coding scheme illustrated in FIG. 10.

In one embodiment, an identifier cell for a given resolution includessufficient information to identify the corresponding cells in the higherlevel grid(s) that contains the cell. Thus, a cell identifier uniquelyidentifies a cell in the entire hierarchical grid without ambiguity.

FIG. 12 shows the method to determine whether a location of a mobiledevice is within the geographical boundary of a region according to oneembodiment.

In FIG. 12, the location (111) of the mobile device (109) is determinedin terms of the longitude coordinate (143) and the latitude coordinate(145).

For a given resolution level (147), the longitude coordinate (143) andthe latitude coordinate (145) are truncated to generate the columnidentifier (149) and the row identifier (151). Applying (155) theresolution level (147) includes truncating the longitude coordinate(143) and the latitude coordinate (145) to the corresponding digits ofprecision to generate the column identifier (149) and the row identifier(151). In one embodiment, the digits corresponding to the top level gridand the bottom level grid at the given resolution are adjusted accordingto FIGS. 9 and 11.

In FIG. 12, the column identifier (149) and the row identifier (151) arecombined to generate the cell identifier (153) of the location (111) ofthe mobile device at the given resolution level (147).

In one embodiment, the database (181) stores a set of cell identifiers(161, . . . , 163) that are associated with the region (101) defined bya predetermined boundary. The server (187) searches (157) the set ofcell identifiers (161, . . . , 163) to find a match with the cellidentifier (153). If a match is found, the location (111) of the mobiledevice (109) is determined to be within the boundary of the region(101).

In one embodiment, the database (181) stores a set of cell identifiers(e.g., 161, . . . , 163, 165, . . . ) associated with respectivedifferent regions (e.g., 101, 103, . . . ). When the cell identifier(153) of the location (111) of the mobile device (109) is found to bematching with a particular cell identifier (e.g., 163 or 165), theregion (e.g., 101 or 103) associated with the particular cell identifier(e.g., 163 or 165) is determined to be the region in which the mobiledevice (141) is located.

In one embodiment, when a cell contains the boundary of two regions(e.g., 101 and 103), the cell identifier of the cell can be associatedwith both regions (e.g., 101 and 103). The system may optionally furtherdetermine which region the cell is in based on the coordinates of thevertexes defining the boundary (or other parameters that define theboundary between the regions).

FIG. 13 illustrates an example of converting the coordinates of alocation to an identifier of a cell and converting the identifier of thecell to the coordinates of a vertex of the cell according to oneembodiment.

In FIG. 13, the location has a latitude coordinate of −51.12345678 and alongitude coordinate of −41.12345678. A resolution at the fifth digitafter the decimal point is applied to the coordinates to generate thetruncated coordinates (−41.12345, −51.12345). The decimal point isremoved to obtain the longitude digits −4112345 and the latitude digits−5112345. Since the length of the equator of the earth is about 40,075km, the cell size near the equator is about 1.11 meters at theresolution corresponding to the fifth digit.

In accordance with the scheme for the top level grid illustrated in FIG.9, the tens digit for the latitude coordinate is padded with one(without considering the sign of the latitude); and the hundreds digitand tens digit, including the sign, of the longitude coordinate ispadded with 18 to generate the row identifier −6 and the columnidentifier 14 for the top level grid.

In accordance with FIG. 10, the row identifiers and column identifiersof the sub-cells in the hierarchical grid correspond to the respectivelatitude digits and longitude digits (1, 1, 2, 3, 4).

In accordance with FIG. 11, the row identifiers and column identifiersof the sub-cells in the bottom hierarchy is padded with 1, if thelongitude and/or the latitude coordinates of the location is not exactlyon the grid lines of the resolution level (e.g., if the longitude orlatitude coordinate has nonzero digits after the fifth digit behind thedecimal point). One is not padded at the last digit when the longitudeand/or the latitude coordinates of the location is exactly on the gridlines of the resolution level (e.g., if the longitude or latitudecoordinate has no nonzero digits after the fifth digit behind thedecimal point). According to this padding scheme, in the northernhemisphere locations on the northern edge of a cell are included in thecell but not the locations on the southern edge. In the southernhemisphere, locations on the southern edge of a cell are included in thecell but not the locations on the northern edges. Locations on theeastern edge of a cell are included in the cell, but not the westernedge.

Thus, the location (−41.12345678, −51.12345678) has the row and columnidentifiers −6112346 and 14112346. The digits of the column identifierare appended to the digits of the row identifier to generate the cellidentifier −611234614112346.

In FIG. 13, the row and column identifiers can be recovered from thecell identifier. The number of digits in the cell identifier divided by2 provides the number of leading digits for the row identifier; and theremaining digits are for the column identifier. From the row identifierand column identifiers, the latitude digits and longitude digits can becomputed via subtraction of the respective padding. The truncatedcoordinates can be computed from the latitude digits and longitudedigits respectively, which can be used to determine the coordinates of avertex of the cell as (−41.12345, −51.12345). Based on the resolution ofthe cell being at 0.00001, the coordinates of other vertexes of the cellcan be determined as (−41.12346, −51.12345), (−41.12346, −51.12344),(−41.12345, −51.12344). The bounding box of the cell and the neighboringcells can also be easily identified based on the coordinates.

FIG. 13 illustrates a way to append the digits of the column identifierto the digits of the row identifier to generate the cell identifier.Alternatively, the row identifier and the column identifier can becombined in other ways that can be reversed to derive the row identifierand the column identifier from the cell identifier.

For example, when the top level column identifiers are mapped to therange 11 to 46 to have a fixed number of two digits for the top levelcolumn, the column identifier is 2411236. Since there is no ambiguity inthe number of digits used to represent the top level column, the toplevel column identifier (24) can be appended after the top level rowidentifier (−6), which is then appended with the row and columnidentifiers of the next level, and so on. Thus, a cell identifier of−6241111223366 can be generated, with the sign then the first threedigits representing the top level row and column, and two digits forsubsequent next level row and column to identifying the subdivisionwithin the higher level cell.

In some embodiments, the row and column identifiers of the bottom levelare not padded in a way illustrated in FIG. 11 to have different ways toaccount for the locations on grid lines at the lowest level resolution.

FIGS. 9-11 and 13 illustrate a grid hierarchy based on a decimalrepresentation of longitude and latitude coordinates. Alternatively, thegrid hierarchy can be constructed in accordance with longitude andlatitude coordinates expressed using other bases, such as binary,ternary, quintal, octal, duodecimal, etc. in a similar way.

Further, in some embodiments, the longitude and latitude coordinates maybe normalized (e.g., in the standardized data range between 0 to 1); andthe grids can be constructed in the space of the normalized longitudeand latitude coordinates.

The hierarchical grid can also be extended to a three-dimensional space.For example, a hierarchical grid can be constructed with regular gridsin the longitude, latitude, altitude space, or in a mapped or normalizedlongitude, latitude, and altitude space.

FIG. 14 shows a system configured to map a location of a mobile deviceto one or more identifications of regions according to one embodiment.In FIG. 14, the mobile device (109) determines the coordinates (171) ofits location (111) based on the wireless signals (179) to and/or from alocation determination system, such as the Global Positioning System(GPS).

The coordinates (171) are converted to a cell identifier (173) of a cellthat contains the location, e.g., in a way as illustrated in FIG. 12 or13.

In the database (181), a set of cell identifiers are stored inassociation with region identifiers (185), where each of the cellidentifiers is associated with one or more of the respective regionswhen the respective cell contains at least a portion of the one or moreof the respective regions.

In one embodiment, the set of cell identifiers are organized as a cellidentifier tree (183) to facilitate the search of a matching identifier.

For example, the cell identifier tree (183) can be constructed as aself-balancing tree for efficient searching of a cell identifiermatching the cell identifier (173) generated from the coordinates (171)of the mobile device (109).

In general, any methods to search for an identifier with a set ofpredetermined identifiers can be used to search for the matching cellidentifier (173).

From the association of the cells with the region identifiers (185) inthe database, the server (187) determines the identification (175) ofthe one or more defined regions that are at least partially in the cellidentified by the cell identifier (173). Thus, the location (111) of themobile device (109) is determined to be within the region(s) identifiedby the identification (175) of the defined region(s).

Similarly, after regions of different sizes and locations arerepresented via the cells in the hierarchical grid, the system can beconfigured to efficiently compute overlapping portions of regions viasearching for cells having the same identifications.

For example, to determine the approximate overlapping between regions,the percentage of overlapping, the square of overlap, etc., the systemis configured to count a number of overlapped cells to determine theoverlapping.

In one embodiment, a polygon or any other shape is approximated by a setof rectangular and/or square cell of different sizes in a suitablecoordinate system (e.g., in longitude latitude space). Each cell isrepresented by a single number as identifier. The identifiers of thecells used to approximate the polygon or shape can be organized as abinary tree, a self-balanced tree, a Red/Black Tree, or other structuresthat are known to provide logarithmic search time to improve thecomputation efficiency in determining whether a point is within thepolygon or shape.

For example, a polygon representing the boundary of United States ofAmerica USA on a map may include 2,000 vertexes. The Ray Castingalgorithm has O(n) complexity to calculate if a point is within thepolygon. When this polygon is approximated via a hierarchical gridsystem discussed above, the polygon can be represented 700 to 2,000,000cells in the longitude latitude space, depending on the requiredprecision. When the polygon is represented by 2,000,000 cells and theircorresponding identification numbers, searching a matching identifier atthe same precision via a binary tree gives log (2,000,000)=21complexity, which is much less than 2,000. Thus, the present disclosureimproves the computational efficiency of identifying a region in which amobile device is located.

FIG. 16 shows a method of mapping a location of a mobile device to aregion according to one embodiment. For example, the method of FIG. 16can be implemented in the system of FIG. 1 and/or FIG. 14, using thegrid system illustrated FIGS. 2-8, and/or the grid system and cellidentifier system illustrated in FIGS. 8-13.

In FIG. 16, a computing apparatus is configured to: identify (221) a setof cells in a grid system that are within the predefined boundary of ageographic region; receive (223) a location (111) of a mobile device(109); convert (225) the location (111)to the identifier of a cell thatcontains the location; and search (227) identifiers of the set of cellsto determine if the cell identifier of the location (111) is in the set.If it is determined (228) that the cell identifier of the location (111)is in the set, the computing apparatus determines (229) that thelocation (111) of the mobile device (109) is in the geographic region.

In one embodiment, the computing apparatus includes at least one of: thedatabase (181) and the server (187).

In one embodiment, the database (181) is configured to store anidentifier of a geographical region (101) having a predefinedgeographical boundary defined by a set of vertexes (e.g., 123) or a setof other parameters, such as a center location and a radius.

The database (181) further stores a set of cell identifiers, each ofwhich identifies a cell that is determined to be within the predefinedgeographical boundary of the geographical region (101).

After the server (187) receives, from a mobile device (109), a location(111) of the mobile device (109), the server (187) converts a set ofcoordinates (143, 145) of the location (111) of the mobile device (109)to a cell identifier (153) of a cell that contains the location (111).In some embodiments, the mobile device (109) generates the cellidentifier (153) at a desired precision level to represent the location(111) of the mobile device (109).

The server (187) determines whether the location (111) of the mobiledevice (109) is within the geographical region (101) based on searchingthe set of cell identifiers to determine if the set has the cellidentifier (153) computed from the coordinates (143, 145) of thelocation (111) of the mobile device (109).

In one embodiment, to convert the set of coordinates (143, 145) of thelocation (143, 145) to the cell identifier (153), the server (187) (orthe mobile device (109)) generates two integers from longitude andlatitude coordinates of the location (111) of the mobile device (109)according to a precision level (e.g., resolution level (147), andcombine the two integers into the first cell identifier (153) withoutusing a floating point number computation.

In one embodiment, each cell using the in the system to approximate theregions and the locations is a rectangle/square area in a longitudelatitude space of locations on the earth. The size of the cell can beunambiguously determined from the cell identifier itself. Further, thelongitude and latitude coordinates of corners of the cell identified bythe cell identifier can be unambiguously determined from the cellidentifier itself.

In one embodiment, the set of cells identified by the set of cellidentifiers to approximate one or more regions (e.g., 101, 103, . . . ,105, . . . , 107) has a plurality of different cell sizes thatcorrespond to a plurality of predetermined cell resolution levels. Eachof the plurality of predetermined cell resolution levels corresponds toa predetermined precision level of longitudes and latitudes of locationson the earth. For example, each of the plurality of predetermined cellresolution levels corresponds to a precision to a predetermined digitafter the decimal point in longitude and latitude coordinates oflocations on the earth.

In one embodiment, a cell identifier itself includes sufficientinformation to determine the resolution level of the cell, thecoordinates of the vertexes of the cell, and the identifiers of theneighboring cells, etc.

In one embodiment, the database (181) stores data mapping each cellidentify in the set of cell identifiers to at least one regionidentifier, where the cell contains a least a part of each of theregions identified by the at least one region identifier. The server(187) is configured to search the set of cell identifiers to find a cellidentifier that matches with the cell identifier (153) computed from thelocation (141) and thus determine at least one region identifierassociated with the matching cell identifier.

For example, in one embodiment, the set of coordinates of the location(111) includes longitude (143) and latitude (145) of the location (111).To converting the coordinates (143, 145) to the cell identifier (153),the server (187) (or the mobile device (109)) selects digits from thelongitude (143) and the latitude (145) of the location (111) inaccordance with a cell resolution level (147) and combines the digitsselected from the longitude (143) and the latitude (145) of the location(111) into an integer representing the cell identifier (153) of thelocation (111).

As illustrated in FIG. 13, selecting the digits from the longitude andthe latitude includes: selecting digits from integer part of thelongitude and a first number of digits from the longitude after thedecimal point of the longitude to form an integer representation of thelongitude at the cell resolution level; and selecting digits frominteger part of the latitude and the same first number of digits fromthe latitude after the decimal point of the latitude to form an integerrepresentation of the longitude at the cell resolution level.

In one embodiment, to generate the column identifier and row identifierof the location (111), a predetermined number (e.g., one) is added to adigit of the integer representation of the latitude that corresponds tothe tens digit of the latitude; and a sign is provided to the integerrepresentation of the latitude according to the sign of the latitude.

In one embodiment, after providing a sign to the integer representationof the longitude according to the sign of the longitude, a predeterminednumber (e.g., eighteen) is added to digits of the integer representationof the longitude that corresponds to the hundreds digit and tens digitof the longitude, in view of the sign provided to the integerrepresentation of the longitude.

In one embodiment, when the latitude coordinate has a non-zero portionthat is discarded during the selection of the latitude digits for theinteger representation of the latitude, one is added to the ones digitof the integer representation of the latitude without considering thesign of the integer representation of the latitude. When the longitudecoordinate has a non-zero portion that is discarded during the selectionof the longitude digits for the integer representation, one is added tothe ones digit of the integer representation of the longitude withoutconsidering the sign of the integer representation of the longitude.

In one embodiment, after the server (187) receives data representing thepredefined geographical boundary of the geographical region, such as thecoordinates of the vertexes of a region having a polygon shape, thecoordinates of the center and the radius of a region having a circularshape, etc., the server (187) identify, in a hierarchy of cell grids,the set of cell identifiers that are determined to be within thepredefined geographical boundary.

In one embodiment, when the set of cells being searched having differentresolutions (cell sizes), the location (111) of the mobile device (109)is converted to a plurality of cell identifiers at the correspondingresolutions; and the server (187) is configured to search a match of anyof the cell identifiers at the corresponding resolutions computed fromthe location (111) of the mobile device (109).

For example, the identifiers of the cells of different sizes/resolutionsto represent the regions can be organized in a single tree; and theidentifiers of the location (111) of the mobile device (109) ofcorresponding sizes/resolutions can be searched concurrently or oneafter another to find a match.

For example, the identifiers of the cells of different sizes/resolutionsto represent the regions can be organized in separate trees according tocell sizes/resolutions; and the identifiers of the location (111) of themobile device (109) of corresponding sizes/resolutions can be searchedconcurrently or one after another in the respective trees forcorresponding sizes/resolutions.

In one embodiment, each grid in the hierarchy of cell grids correspondsto a rectangle/square grid in longitude latitude space of locations onthe earth with a predetermined resolution level that corresponds to aprecision level in a floating point decimal representation of longitudeand latitude coordinates.

FIG. 17 illustrates the measurement of the location of the mobile devicewith a level of uncertainty.

As illustrated in FIG. 17, the measurement of the location (111) of amobile device (109) may be represented by a set of coordinates (e.g.,143, 145) of a point (111) that is considered to be the most likelycandidate of the location (111) of the mobile device (109) and a levelof accuracy or uncertainty in the measurement.

The level of accuracy of the location measurement indicates that theactual location of the mobile device (109) is within an area (231) thatcontains the point (111) identified by the set of coordinates (e.g.,143, 145). Any point within area (231) has a probability of being theactual location (111) of the mobile device (109); and the probability ofthe actual location of the mobile device (109) being outside the area isconsidered negligible.

The level of accuracy of the location measurement may be identified viaa radius of the area (231) for the point (111) specified by the set ofcoordinates (e.g., when the accuracy of the measurement isnon-directional in the space).

In some instances, the measured coordinates (e.g., 143, 145) may havedifferent accuracy levels in different measurement directions in thespace (e.g., longitudinal direction, latitudinal direction, and/oraltitudinal direction).

The possible area (231) of the actual location of the mobile device(109) may be constructed as a circular, elliptical, square, or rectanglearea in a two dimensional location measurement space, or a spherical,ellipsoidal, cube, or rectangular cuboid in a three dimensional locationmeasurement space.

A distribution of probability of the actual location of the mobiledevice (109) being at a point within the area can be constructed byfitting a predetermined distribution function on the area (231), wherethe distribution function identifies the largest probability at thepoint (111) specified by the set of coordinates (e.g., 143, 145) anddiminished probabilities at or near the boundary of the area (231) andoutside of the area (231).

The measurement of the location (111) of the mobile device (109) can beobtained via a position determination system (e.g., illustrated inFIG. 1) in which the mobile device (109) receives signals from, and/ortransmits signals to, transmitters/receivers positioned at referencelocations in the space.

For example, the position determination system may be a satellitepositioning system using signals (179) from satellites (e.g., 117) tocompute the coordinates of the location (111) of the mobile device (109)with an estimated accuracy. For example, the satellite positioningsystem may be a GPS, GLONASS, Galileo, Beidou, IRNSS, or QZSS system.

For example, the position determination system may be a cellularpositioning system using signals to or from base stations (e.g., 113) tocompute the coordinates of the location (111) of the mobile device (109)with an estimated accuracy.

When the possible area (231) of the actual location of the mobile device(109) is large (e.g., relative to the resolution of boundaryrepresentation of a predefined geographical region), it is insufficientto conclude with a sufficient confidence level as to whether the actuallocation of the mobile device (109) is within the geographical regionbased solely on a determination of whether the most likely point (111)of the actual location of the mobile device (109) is in the geographicalregion.

To evaluate the confidence level of a determination as to whether or notthe actual location of the mobile device (109) is within thegeographical region, a plurality of sample points in the possible area(231), including the point (111) specified by the set coordinatesprovided in the location measurement, can be selected for adetermination of whether the sample points are within the predefinedboundary of the geographical region. When a subset of the sample pointsare found to be within the predefined boundary of the geographicalregion, the confidence level of the actual location of the mobile device(109) is within the geographical region can be evaluated based on a setof predetermined weights assigned to the sample points that aredetermined to be within the predefined boundary of the geographicalregion.

FIG. 18 illustrates a method to compute the confidence level of a mobiledevice being in a region having a predefined geographical boundary.

FIG. 18 shows a set of sample points (e.g., 111, 241, 243, 245, and 247)that are located within the area (231) specified by the coordinates ofthe most likely point (111) of the location of the mobile device (109)and the accuracy level of the measurement of the coordinates of the mostlikely point (111). The sample points (e.g., 111, 241, 243, 245, and247) are shown in FIG. 18 in relation with a set of cells (e.g., 127)that are used represent a region (101) having a predefined geographicalboundary according to a selected resolution level. The smallest size ofthe cells (e.g., 127) correspond the resolution level at which thecollection of cells (e.g., 127) approximates the region (101) that isdefined by its geographical boundary.

Although FIG. 18 illustrates an example in which a set of cells (e.g.,127) having the same cell size is used to approximate the region, ahierarchical cells (e.g., illustrated in FIG. 7) having different cellsizes can be used to reduce the number of cells in representing theregion without reducing resolution.

In FIG. 18, some of the sample points (e.g., 241, 243, 247) are locatedon the boundary of the area (231) specified by the coordinates of themost likely point (111) of the location of the mobile device (109) andthe accuracy level of the measurement of the coordinates of the mostlikely point (111); and some of the sample points are located inside thearea (e.g., 111, 245). In general, it is not necessary to select thesample points (e.g., 241, 243, 247) from the boundary of the area (231).

Each of the sample points (e.g., 241, 243, 245, 247) can be separatelydetermined as to whether not the sample point is inside or outside ofthe geographical region, based on whether or not a cell identifierconverted from the coordinates of the sample point matches with a cellidentifier of the set of cells (e.g., 127) that collectively representthe region (e.g., using the techniques discussed in connection withFIGS. 1-16).

Each of the sample points (e.g., 111, 241, 243, 245, 247) is provided aweight based on the probability distribution of a portion of the areathat is in the vicinity of a sample point and thus is controlled by thecorresponding sample point. The probability distribution of the portionof the area (231) indicates the probability that the actual location ofthe mobile device (109) is in the portion of the area (231).

The sum of the weights assigned to the sample points (e.g., 111, 243,245, 247) located within the set of cells (e.g., 127) corresponds theprobability that the actual location of the mobile device (109) is inthe portions of the areas controlled by the sample points located withinthe region (101) represented by the set of cells (e.g., 127). Thus, thesum of the weights represents the confidence level that the actuallocation of the mobile device (109) is the region; and the sum of theweights assigned to the sample points (e.g., 241) located outside of theset of cells (e.g., 127) represents the confidence level that the actuallocation of the mobile device (109) is outside of the region.

Increasing the number of sample points used in evaluating the confidencelevel of a determination of whether the actual location of the mobiledevice (109) is inside or outside of the region (101) can increase theaccuracy in the determination of the confidence level but with anincreased computation cost.

Preferably, the number of sample points and their distribution areselected based on the size (233) of the area (231) relative to theresolution of the cells (e.g., 139) near the area (231). The quantityand distribution of sample points can be selected to match theresolution of the one or more cells that contain the point (111) and/orother sample points. An iterative process can be used to add samplepoints based on the cell sizes that contain the already added samplepoints.

For example, when the point (111) is determined to be within the cell(139), the size of the cell (139) can be compared to the size of thearea (233) to determine whether sample points are required (e.g.,whether the size ratio is above a threshold); and if so, sample points(e.g., 241, 243, 247) can be added on the boundary of the area (231);and additional sample points (e.g., 245) can be added to match with thecell resolution of the point (111) and/or other sample points (e.g.,243, 247) (e.g., adding more sample points to reduce the ratio betweenthe size(s) of the cell(s) in which the sample points are located andthe distance(s) among the sample points to below a threshold).

Preferably, the locations of the sample points relative to the centerpoint (111) and the boundary of the area (243), as well as their weightsare predetermined. Thus, the coordinates of the sample points can becomputed by scaling the offsets of the sample points from the centerpoint according to the size (233) of the area (231).

FIG. 19 shows a method to identify regions with corresponding confidencelevels of a mobile device being in the respective regions.

For example, the method of FIG. 19 can be implemented in a systemillustrated in FIG. 1 using sample points illustrated in FIG. 18, wherethe determination of whether any of the sample point is within oroutside of a region can be performed using the techniques illustrated inFIG. 12 where the coordinates of the sample point are converted into acell identifier for a match in cell identifiers associated with theregion.

In FIG. 19, the method includes: storing (251) mapping data associatingcell identifiers (161, . . . , 163, 165, . . . ) to a plurality ofgeographic regions (101, 103, . . . ) that contain respective cellsidentified by the cell identifiers in a grid reference system (e.g.,illustrated in FIGS. 2-11); and receiving (253) location data includinga set of coordinates (e.g., 143, 145) of a location (111) of a mobiledevice (109) and an accuracy indicator (231) of the location (111).

The method further includes: generating (255) coordinates of a pluralityof possible locations (e.g., 111, 241, 243, 245, 247) of the mobiledevice according to the accuracy indicator (231) of the location of themobile device (109); converting (257) the coordinates (e.g., 143, 145)of the possible locations (e.g., 111, 241, 243, 245, 247) intocorresponding cell identifiers (e.g., 153); and looking (259) up one ormore geographic regions identified by the corresponding matching cellidentifiers (161, . . . , 163, 165, . . . ) in the mapping data (e.g.,using the technique illustrated in FIG. 12 or 14).

For each respective region in the one or more geographic regions thatcontain any of the possible locations, the method further includescomputing (261) a confidence level of the location data indicating thatthe mobile device (109) is located within the respective region, basedon the weight of the possible locations in the respective region.

When cells from a hierarchy of grids of different resolutions are usedto represent a region, a coarse level cell containing fine level cellshas a cell identifier that matches a portion of each of the identifiersof the fine level cells. The match indicates that the coarse level cellcontaining the fine level cells. Thus, the search for a match to a finelevel cell identifier converted from the coordinates of a sample point(e.g., 111, 241, 243, 245, 247) can be used to identify a coarse or finelevel cell that contains the same point (e.g., 111, 241, 243, 245, 247).

When a confidence level of the location data indicating that the mobiledevice (109) is located within a particular respective region is above athreshold, the mobile device (109) is identified as being located withinthe particular respective region as a result of the location data.Preferably, the threshold is selected such that mobile device (109)cannot be considered to be located within two non-overlapping regionsaccording to the same location data, even though both regions maycontain different portions of the sample points (e.g., when the area(231) is at the boundary between the regions and partially in the eachof the regions).

In some instances, a mobile device (109) may report its location as aset of coordinates of the most likely point (111) without providing anaccuracy indicator. A predictive model is used to estimate the accuracylevel of the coordinates based on characteristics of the mobile device(109) and the characteristics of the signal environment of the point(111).

FIG. 20 illustrates region attributes and device attributes for theestimation of the accuracy level of coordinates of a location. Theregion attributes and device attributes related to the accuracy oflocation determination can be used in a statistical predictive model toestimate accuracy level of the coordinates reported by a mobile device.

In FIG. 20, the accuracy level (171) of a set of location coordinates(140) of a mobile device (109) relates to the device attributes of themobile device (109), such as device version (271), the identification(273) of the receiver of signals used for location determination (e.g.,GPS receiver), the configuration (275) of the mobile device (109) (e.g.,how the receiver and/or antenna is configured on the mobile device(109)), software version (277) of computation algorithms for processingthe location determination signals to generate the coordinates, etc. Thedevice attributes have impact on the accuracy level (171) and/or areindicative of factors that have impact on the accuracy level (171).

In FIG. 20, the accuracy level (171) of the set of location coordinates(140) of the mobile device (109) also relates to the region attributesof the region (101) that contains the coordinates (140) of the mobiledevice (109), such as the attribute (281) of location determinationsignals available in the region (101), the attribute (283) of signaltransmitters that transmit the location determination signals (e.g., GPSsignals (179), cellular signals), the attribute (285) of the environmentin the region (101) that has impact on the reception of locationdetermination signals (e.g., whether the region is a high rise urbanenvironment or an area under tree cover), etc. The region attributeshave impact on the accuracy level (171) and/or are indicative of factorsthat have impact on the accuracy level (171).

A predictive model can be trained, using any statistical method known inthe field in general, using location data reported by a population ofmobile devices that report not only the coordinates of their locationsbut also the accuracy levels of the reported coordinates. Thestatistical training determines the parameters of the predictive modelin making predictions to best match the reported accuracy levels of themobile devices and the predicted accuracy levels that are predictedbased on the region attributes of the reported locations and the deviceattributes of the mobile devices reporting the locations.

In some implementations, machine learning techniques can be used totrain and/or improve a predictive model.

After the parameters of the predictive model are determined from thetraining data set, the predictive model can be used to estimate anaccuracy level (171) when a mobile device (109) reports its coordinates(140) without the accuracy level (171). The coordinates (140) can beused to identify the region (101) that contains the coordinates (140) ofthe mobile device (109) and determine the attributes (281, 283, . . . ,285) of the region (101). The mobile device (109) reports its deviceattributes (271, 273, 275, . . . , 277). Applying the device attributes(271, 273, 275, . . . , 277) and the region attributes (281, 283, . . ., 285) in the predictive model provides an estimate of the accuracylevel (171), which can be used determine the confidence level as towhether the actual location of the mobile device (109) is within apredetermined boundary of a particular region (e.g., 127).

FIG. 21 shows a method to evaluate the accuracy level of coordinates ofa location using a predictive model.

For example, the method of FIG. 21 can be implemented in a system ofFIG. 1 using a predictive model discussed above in connection with FIG.20.

In FIG. 21, the method includes: storing (301) region attributes (281,283, . . . , 285) of regions (e.g., 101), where the attributes (281,283, . . . , 285) relate to accuracy of location determination of mobiledevices (e.g., 109) positioned in the regions (e.g., 101).

After receiving (303) coordinates (e.g., 140) and accuracy levels (171)of the coordinates (e.g., 140) of locations (e.g., 111) of the mobiledevices (e.g., 109) and receiving (305) device attributes (271, 273,275, . . . , 277) of the mobile devices (e.g., 109) that relate toaccuracy of location determination of the mobile devices (e.g., 109)positioned in the regions (e.g., 101), the method includes training(307) a predictive model to predict the accuracy levels (e.g., 171)based on the region attributes (281, 283, . . . , 285) and the deviceattributes (271, 273, 275, . . . , 277).

The training (307) adjusts the parameters of the predictive model tominimize the difference between the accuracy levels (e.g., 171) obtainedwith the location coordinates (e.g., 140) and the accuracy levelcomputed from the predictive model using the respective deviceattributes (271, 273, 275, . . . , 277) of mobile devices (e.g., 109)reporting the respective coordinates (e.g., 140) and the respectiveregion attributes (281, 283, . . . , 285) of the respective regions(e.g., 101) in which the respective coordinates (e.g., 140) are located.

Subsequently, in response to receiving (309) coordinates (140) for alocation (101) of a mobile device (109) with attributes (271, 273, 275,. . . , 277) of the mobile device (109), the method further includesdetermining (311) a region (101) that contains the coordinates (140) ofthe location (111) of the mobile device (109) and applying (313) theattributes (271, 273, 275, . . . , 277) of the mobile device (109) andattributes (281, 283, . . . , 285) of the region (101) to the predictivemodel to obtain an accuracy level (171) of the coordinates (140) of thelocation (111) of the mobile device (109).

In some implementations, the location coordinates (140) include avertical location for a determination of whether the mobile device (109)is in a particular floor of a multi-floor building.

In some implementations, the prediction of the accuracy level (171)and/or the location coordinates (140) can be based on the locationhistory of a mobile device (109) in relation with the current time ofthe day, the date of the week, the week of the month, etc.

In an application that uses location data, a configurable parameter canbe used to identify a confidence threshold. When the accuracy level oflocation data is above a confidence threshold, the location data isconsidered to be of high-confidence. The application may be configured,for example, to use only the high-confidence data that would still allowit to perform the calculation and thereby excluding lower-confident datathat would be superfluous in calculating the result without impactingthe statistical significance. If there isn't enough high-confidence datato produce a result, the application can be configured to provide noresult.

The server (187) and/or the database (181) can be implemented as acomputer apparatus in the form of a data processing system illustratedin FIG. 15.

FIG. 15 illustrates a data processing system according to oneembodiment. While FIG. 15 illustrates various components of a computersystem, it is not intended to represent any particular architecture ormanner of interconnecting the components. One embodiment may use othersystems that have fewer or more components than those shown in FIG. 15.

In FIG. 15, the data processing system (200) includes an inter-connect(201) (e.g., bus and system core logic), which interconnects one or moremicroprocessors (203) and memory (204). The microprocessor (203) iscoupled to cache memory (209) in the example of FIG. 15.

In one embodiment, the inter-connect (201) interconnects themicroprocessor(s) (203) and the memory (204) together and alsointerconnects them to input/output (I/O) device(s) (205) via I/Ocontroller(s) (207). I/O devices (205) may include a display deviceand/or peripheral devices, such as mice, keyboards, modems, networkinterfaces, printers, scanners, video cameras and other devices known inthe art. In one embodiment, when the data processing system is a serversystem, some of the I/O devices (205), such as touch screens, printers,scanners, mice, and/or keyboards, are optional.

In one embodiment, the inter-connect (201) includes one or more busesconnected to one another through various bridges, controllers and/oradapters. In one embodiment the I/O controllers (207) include a USB(Universal Serial Bus) adapter for controlling USB peripherals, and/oran IEEE-1394 bus adapter for controlling IEEE-1394 peripherals.

In one embodiment, the memory (204) includes one or more of: ROM (ReadOnly Memory), volatile RAM (Random Access Memory), and non-volatilememory, such as hard drive, flash memory, etc.

Volatile RAM is typically implemented as dynamic RAM (DRAM) whichrequires power continually in order to refresh or maintain the data inthe memory. Non-volatile memory is typically a magnetic hard drive, amagnetic optical drive, an optical drive (e.g., a DVD RAM), or othertype of memory system which maintains data even after power is removedfrom the system. The non-volatile memory may also be a random accessmemory.

The non-volatile memory can be a local device coupled directly to therest of the components in the data processing system. A non-volatilememory that is remote from the system, such as a network storage devicecoupled to the data processing system through a network interface suchas a modem or Ethernet interface, can also be used.

In this description, some functions and operations are described asbeing performed by or caused by software code to simplify description.However, such expressions are also used to specify that the functionsresult from execution of the code/instructions by a processor, such as amicroprocessor.

Alternatively, or in combination, the functions and operations asdescribed here can be implemented using special purpose circuitry, withor without software instructions, such as using Application-SpecificIntegrated Circuit (ASIC) or Field-Programmable Gate Array (FPGA).Embodiments can be implemented using hardwired circuitry withoutsoftware instructions, or in combination with software instructions.Thus, the techniques are limited neither to any specific combination ofhardware circuitry and software, nor to any particular source for theinstructions executed by the data processing system.

While one embodiment can be implemented in fully functioning computersand computer systems, various embodiments are capable of beingdistributed as a computing product in a variety of forms and are capableof being applied regardless of the particular type of machine orcomputer-readable media used to actually effect the distribution.

At least some aspects disclosed can be embodied, at least in part, insoftware. That is, the techniques may be carried out in a computersystem or other data processing system in response to its processor,such as a microprocessor, executing sequences of instructions containedin a memory, such as ROM, volatile RAM, non-volatile memory, cache or aremote storage device.

Routines executed to implement the embodiments may be implemented aspart of an operating system or a specific application, component,program, object, module or sequence of instructions referred to as“computer programs.” The computer programs typically include one or moreinstructions set at various times in various memory and storage devicesin a computer, and that, when read and executed by one or moreprocessors in a computer, cause the computer to perform operationsnecessary to execute elements involving the various aspects.

A machine readable medium can be used to store software and data whichwhen executed by a data processing system causes the system to performvarious methods. The executable software and data may be stored invarious places including for example ROM, volatile RAM, non-volatilememory and/or cache. Portions of this software and/or data may be storedin any one of these storage devices. Further, the data and instructionscan be obtained from centralized servers or peer to peer networks.Different portions of the data and instructions can be obtained fromdifferent centralized servers and/or peer to peer networks at differenttimes and in different communication sessions or in a same communicationsession. The data and instructions can be obtained in entirety prior tothe execution of the applications. Alternatively, portions of the dataand instructions can be obtained dynamically, just in time, when neededfor execution. Thus, it is not required that the data and instructionsbe on a machine readable medium in entirety at a particular instance oftime.

Examples of computer-readable media include but are not limited torecordable and non-recordable type media such as volatile andnon-volatile memory devices, read only memory (ROM), random accessmemory (RAM), flash memory devices, floppy and other removable disks,magnetic disk storage media, optical storage media (e.g., Compact DiskRead-Only Memory (CD ROM), Digital Versatile Disks (DVDs), etc.), amongothers. The computer-readable media may store the instructions.

The instructions may also be embodied in digital and analogcommunication links for electrical, optical, acoustical or other formsof propagated signals, such as carrier waves, infrared signals, digitalsignals, etc. However, propagated signals, such as carrier waves,infrared signals, digital signals, etc. are not tangible machinereadable medium and are not configured to store instructions.

In general, a machine readable medium includes any mechanism thatprovides (i.e., stores and/or transmits) information in a formaccessible by a machine (e.g., a computer, network device, personaldigital assistant, manufacturing tool, any device with a set of one ormore processors, etc.).

In various embodiments, hardwired circuitry may be used in combinationwith software instructions to implement the techniques. Thus, thetechniques are neither limited to any specific combination of hardwarecircuitry and software nor to any particular source for the instructionsexecuted by the data processing system.

The description and drawings are illustrative and are not to beconstrued as limiting. Numerous specific details are described toprovide a thorough understanding. However, in certain instances, wellknown or conventional details are not described in order to avoidobscuring the description. References to one or an embodiment in thepresent disclosure are not necessarily references to the sameembodiment; and, such references mean at least one.

The use of headings herein is merely provided for ease of reference, andshall not be interpreted in any way to limit this disclosure or thefollowing claims.

Reference to “one embodiment” or “an embodiment” means that a particularfeature, structure, or characteristic described in connection with theembodiment is included in at least one embodiment of the disclosure. Theappearances of the phrase “in one embodiment” in various places in thespecification are not necessarily all referring to the same embodiment,and are not necessarily all referring to separate or alternativeembodiments mutually exclusive of other embodiments. Moreover, variousfeatures are described which may be exhibited by one embodiment and notby others. Similarly, various requirements are described which may berequirements for one embodiment but not other embodiments. Unlessexcluded by explicit description and/or apparent incompatibility, anycombination of various features described in this description is alsoincluded here. For example, the features described above in connectionwith “in one embodiment” or “in some embodiments” can be all optionallyincluded in one implementation, except where the dependency of certainfeatures on other features, as apparent from the description, may limitthe options of excluding selected features from the implementation, andincompatibility of certain features with other features, as apparentfrom the description, may limit the options of including selectedfeatures together in the implementation.

In the foregoing specification, the disclosure has been described withreference to specific exemplary embodiments thereof. It will be evidentthat various modifications may be made thereto without departing fromthe broader spirit and scope as set forth in the following claims. Thespecification and drawings are, accordingly, to be regarded in anillustrative sense rather than a restrictive sense.

What is claimed is:
 1. A method implemented in a computing device, themethod comprising: identifying, by the computing device, a region inwhich a mobile device is located, by at least: determining coordinatesof the mobile device; determining an accuracy indicator of thecoordinates of the mobile device; calculating a plurality of locationsfrom the accuracy indicator and the coordinates of the mobile device,wherein the locations include a location specified by the coordinates ofthe mobile device and at least one location different from the locationspecified by the coordinates of the mobile device; determining one ormore regions that contain the plurality of locations respectively, theone or more regions including the region in which the mobile device issubsequently identified to be located; evaluating, based on a portion ofthe plurality of locations that is in the region, a confidence levelthat the mobile device is located in the region; and making adetermination that the mobile device is located within the region basedon the confidence level being above a threshold.
 2. The method of claim1, wherein the accuracy indicator identifies a possible range of atleast one of: longitudinal coordinate of the mobile device; latitudinalcoordinate of the mobile device; and altitudinal coordinate of themobile device.
 3. The method of claim 1, further comprising: storing, inthe computing device, mapping data that connect identifiers of cells ina grid reference system to regions that contain respective cellsidentified by the identifiers in the grid reference system; andconverting, by the computing device, coordinates of the plurality oflocations into respective cell identifiers in the grid reference system,wherein respective cells in the grid reference system identified by therespective cell identifiers contain respective coordinates from whichthe respective cell identifiers are converted; wherein the one or moreregions are determined based on the one or more regions being connectedto the cell identifiers in the mapping data.
 4. The method of claim 3,further comprising: identifying a size of a cell that has an identifierin the mapping data and contains the coordinates of the mobile device;and selecting the at least one location, different from the locationspecified by the coordinates of the mobile device, based on the size ofthe cell.
 5. The method of claim 4, wherein each respective location inthe plurality of locations is assigned a predetermined weight forevaluation of the confidence level based on whether the respectivelocation is in the region.
 6. The method of claim 5, wherein theconfidence level is based on one of: a sum of weights assigned tolocations in the portion located within the region; and a sum of weightsassigned to locations in a portion of the plurality of locations that isoutside of the region.
 7. The method of claim 5, wherein thepredetermined weight is based on a distribution of probability of themobile device being at locations in a local area represented by therespective location.
 8. The method of claim 3, further comprising:receiving device attributes of the mobile device; identifying regionattributes of the coordinates of the mobile device; and determining theaccuracy indicator based on the device attributes and the regionattributes.
 9. The method of claim 8, wherein the determining of theaccuracy indicator includes applying the device attributes and theregion attributes in a predictive model trained using location data froma plurality of mobile devices having different device attributes andlocated in a plurality of regions having different region attributes.10. A non-transitory computer storage medium storing instructions which,when executed by a computing device, instruct the computing device toperform a method, the method comprising: identifying, by the computingdevice, a region in which a mobile device is located, by at least:determining coordinates of the mobile device; determining an accuracyindicator of the coordinates of the mobile device; calculating aplurality of locations from the accuracy indicator and the coordinatesof the mobile device, wherein the locations include a location specifiedby the coordinates of the mobile device and at least one locationdifferent from the location specified by the coordinates of the mobiledevice; determining one or more regions that contain the plurality oflocations respectively, the one or more regions including the region inwhich the mobile device is subsequently identified to be located;evaluating, based on a portion of the plurality of locations that is inthe region, a confidence level that the mobile device is located in theregion; and making a determination that the mobile device is locatedwithin the region based on the confidence level being above a threshold.11. The non-transitory computer storage medium of claim 10, wherein themethod further comprises: storing, in the computing device, mapping datathat connect identifiers of cells in a grid reference system to regionsthat contain respective cells identified by the identifiers in the gridreference system; and converting, by the computing device, coordinatesof the plurality of locations into respective cell identifiers in thegrid reference system, wherein respective cells in the grid referencesystem identified by the respective cell identifiers contain respectivecoordinates from which the respective cell identifiers are converted;wherein the one or more regions are determined based on the one or moreregions being connected to the cell identifiers in the mapping data. 12.The non-transitory computer storage medium of claim 11, wherein themethod further comprises: identifying a size of a cell that has anidentifier in the mapping data and contains the coordinates of themobile device; and selecting the at least one location, different fromthe location specified by the coordinates of the mobile device, based onthe size of the cell.
 13. The non-transitory computer storage medium ofclaim 11, wherein the method further comprises: receiving deviceattributes of the mobile device; identifying region attributes of thecoordinates of the mobile device; and determining the accuracy indicatorbased on the device attributes and the region attributes.
 14. Acomputing device, comprising: at least one microprocessor; memorystoring instructions which, when executed by the at least onemicroprocessor, instruct the computing device to: calculate a pluralityof locations from coordinates of a mobile device and an accuracyindicator of the coordinates of the mobile device, wherein the locationsinclude a location specified by the coordinates of the mobile device andat least one location different from the location specified by thecoordinates of the mobile device; determine one or more regions thatcontain the plurality of locations respectively, the one or more regionsincluding a region in which the mobile device is subsequently identifiedto be located; evaluate, based on a portion of the plurality oflocations that is in the region, a confidence level that the mobiledevice is located in the region; and identify the mobile device as beinglocated within the region based on the confidence level being above athreshold.
 15. The computing device of claim 14, wherein theinstructions are further configured to instruct the computing device to:receive device attributes of the mobile device; identify regionattributes of the coordinates of the mobile device; and determine theaccuracy indicator based on the device attributes and the regionattributes.
 16. The computing device of claim 15, wherein the accuracyindicator is determined by applying the device attributes and the regionattributes in a predictive model trained using location data from aplurality of mobile devices having different device attributes andlocated in a plurality of regions having different region attributes.17. The computing device of claim 14, wherein the computing device isconfigured to store mapping data that connect identifiers of cells in agrid reference system to regions that contain respective cellsidentified by the identifiers in the grid reference system; and theinstructions are further configured to instruct the computing device to:convert coordinates of the plurality of locations into respective cellidentifiers in the grid reference system, wherein respective cells inthe grid reference system identified by the respective cell identifierscontain respective coordinates from which the respective cellidentifiers are converted; wherein the one or more regions aredetermined based on the one or more regions being connected to the cellidentifiers in the mapping data.
 18. The computing device of claim 17,wherein the instructions are further configured to instruct thecomputing device to: identify a size of a cell that has an identifier inthe mapping data and contains the coordinates of the mobile device; andselect the at least one location, different from the location specifiedby the coordinates of the mobile device, based on the size of the cell.19. The computing device of claim 18, wherein each respective locationin the plurality of locations is assigned a predetermined weight forevaluation of the confidence level based on whether the respectivelocation is in the region; and the predetermined weight is based on adistribution of probability of the mobile device being at locations in alocal area represented by the respective location.
 20. The computingdevice of claim 19, wherein the confidence level is based on one of: asum of weights assigned to locations in the portion located within theregion; and a sum of weights assigned to locations in a portion of theplurality of locations that is outside of the region.